Ra Local Talking Head Cut logo

Ra Local Talking Head Cut

CommunityPopular
Pluviobyte
ra-local-talking-head-cut

Produce a polished local talking-head or narrated screen-recording rough cut without a cloud editor. Use when Codex must clean Chinese or mixed Chinese-English speech, correct product terminology before semantic editing, compress pauses without making speech breathless, preserve source resolution and frame rate, normalize dialogue loudness, generate final-audio subtitle artifacts, or benchmark local output against ChatCut/video-use/chengfeng/AI剪口播.

Overview

PublisherPluviobyte
Repositoryrnskill
Skill namera-local-talking-head-cut
Stars
1.6K
Forks
181
Bundled files
10
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Pluviobyte on GitHub. Read the source before you install it.

Installation

Install the Ra Local Talking Head Cut AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Pluviobyte/rnskill.git /tmp/rnskill
mkdir -p .claude/skills
cp -r /tmp/rnskill/skills/ra-local-talking-head-cut .claude/skills/ra-local-talking-head-cut
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ra Local Talking Head Cut in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ra Local Talking Head Cut on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ra Local Talking Head Cut is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

本地口播精剪

Deliver a reproducible local workflow, not an editing product. Keep upstream skills untouched and write every job under the source project's engineering directory.

Required workflow

  1. Probe the source. Preserve width, height, and frame rate unless the user explicitly requests a delivery conversion.
  2. Transcribe with the installed video-use/helpers/transcribe.py using volc.seedasr.auc and word timestamps. Reuse its cached normalized JSON.
  3. Run scripts/prepare_transcript.py with references/default-glossary.json. It must create review.md, precut-review.srt, corrected-script.md, uncertain-terms.md, and subtitle-approval.json.
  4. Give the user the source video plus precut-review.srt and the readable review. Correct only well-supported terminology. Never guess an uncertain model or product name. Stop here and wait for explicit user approval. Do not build an EDL or render a cut while approval is pending.
  5. Resolve uncertain terms by adding confirmed mappings to a job-local copy of the glossary, then rerun preparation so both the script and pre-cut SRT are regenerated together. Preparation always resets approval. After the user confirms the regenerated SRT, set subtitle-approval.json to approved: true, record the confirmation time, and leave unresolved_terms empty. Never keep approval across regeneration.
  6. Write optional decisions.json for semantic deletions. Delete repeated starts and failed takes before their later complete version; preserve unique meaning. Use normalized word indexes only inside artifacts, never in the user-facing report.
  7. Run scripts/build_edl.py with the approval file. The script must refuse to continue unless subtitles are approved and all uncertainty is resolved. Its default pacing keeps pauses up to 550 ms, compresses longer pauses to 380-450 ms, removes only unambiguous 呃/额, and pads source head/tail. Do not replace this with blanket deletion of every pause above 200 ms.
  8. Run scripts/render_cut.py without transitions to create a hard-cut preview. It preserves source dimensions/fps, applies 15 ms audio fades at every cut, uses light dialogue cleanup, and performs two-pass loudness normalization to -16 LUFS / -1.5 dBTP. Stronger denoise or gates are candidates, not defaults: reject a candidate when the same ASR alignment test drops by more than 0.01 or falls below 0.90.
  9. Run scripts/analyze_visual_cuts.py on the hard-cut preview. Inspect its contact sheet and use only its recommended 80-120 ms transitions, capped at three per video. Rerender with --transitions only when a valid semantic cut also has a large visual discontinuity. Do not add blanket transitions.
  10. Run scripts/qc.py against the chosen clean final MP4 and pass it the same transition JSON when transitions were used. Require matching dimensions/fps, valid audio/video, expected duration, and loudness within the gate. Inspect both the general and transition contact sheets.
  11. Run scripts/generate_final_subtitles.py against the exact final MP4 and approved corrected-script.md. It delegates timing/alignment to ra-audio-to-subtitles and refuses delivery unless caption-qc.json is PASS. ASR is the timing source; approved script text is the display source. Never reuse precut-review.srt after timeline edits.
  12. When the deliverable needs visible subtitles, run skill-captions with the final captions.json and PASS caption-qc.json. Use anchor-dark unless the contract selects another style. Preview a representative frame, burn the derivative, and require caption-render-qc.json to pass. Keep the clean rough cut and portable SRT.

Commands

bash
SKILL_DIR="<this skill directory>"
JOB="<engineering job directory>"
WORDS="$JOB/transcripts/<source-name>.json"

python3 "$SKILL_DIR/scripts/prepare_transcript.py" \
  "$WORDS" --out-dir "$JOB/transcript-review" \
  --glossary "$SKILL_DIR/references/default-glossary.json"

python3 "$SKILL_DIR/scripts/build_edl.py" \
  <source.mp4> "$WORDS" --out "$JOB/edl.json" \
  --approval "$JOB/transcript-review/subtitle-approval.json" \
  --decisions "$JOB/decisions.json"

python3 "$SKILL_DIR/scripts/render_cut.py" \
  "$JOB/edl.json" --out "$JOB/local-hard-cut-preview.mp4"

CODEX_PY="$HOME/.cache/codex-runtimes/codex-primary-runtime/dependencies/python/bin/python3"
"$CODEX_PY" "$SKILL_DIR/scripts/analyze_visual_cuts.py" \
  "$JOB/local-hard-cut-preview.mp4" --edl "$JOB/edl.json" \
  --out-dir "$JOB/visual-cut-qc"

python3 "$SKILL_DIR/scripts/render_cut.py" \
  "$JOB/edl.json" --out "$JOB/local-benchmark.mp4" \
  --transitions "$JOB/visual-cut-qc/visual-cut-qc.json"

python3 "$SKILL_DIR/scripts/qc.py" \
  <source.mp4> "$JOB/local-benchmark.mp4" \
  --edl "$JOB/edl.json" \
  --transitions "$JOB/visual-cut-qc/visual-cut-qc.json" \
  --out-dir "$JOB/qc"

python3 "$SKILL_DIR/scripts/generate_final_subtitles.py" \
  "$JOB/local-benchmark.mp4" \
  --script "$JOB/transcript-review/corrected-script.md" \
  --approval "$JOB/transcript-review/subtitle-approval.json" \
  --out-dir "$JOB/captions"

"$CODEX_PY" ".claude/skills/skill-captions/scripts/render_captions.py" \
  "$JOB/local-benchmark.mp4" "$JOB/captions/captions.json" \
  --qc "$JOB/captions/caption-qc.json" --style anchor-dark \
  --out "$JOB/local-benchmark-captioned.mp4" \
  --preview "$JOB/qc/caption-preview.png" --preview-at 15

Omit --decisions when no semantic repeats or failed takes exist.

Decision gates

  • Prefer missed filler over lost meaning.
  • Treat transcript correction and playback deletion as separate operations.
  • Do not create an EDL while subtitle approval is false or unresolved terms remain.
  • Treat precut-review.srt as source-timeline review material only.
  • Do not burn captions while caption-qc.json is absent or not PASS.
  • Do not accept audio cleanup whose same-input ASR coverage regresses by more than one percentage point, even when it sounds superficially quieter.
  • Do not add transitions to every cut; a transition requires both a valid semantic boundary and measured visual discontinuity.
  • Do not call a rough-cut subtitle timeline production-ready.
  • Do not deliver when cut-qc.json reports fail.
  • Keep intermediates in 01-内容生产/视频工作台/制作中/<日期-主题>/; archive only the user-approved final under 视频工作台/已制作/月上旬或月下旬/日期-主题/.

Read references/artifact-contract.md when integrating another renderer or modifying artifact schemas.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ra Local Talking Head Cut AI skill do?

Produce a polished local talking-head or narrated screen-recording rough cut without a cloud editor. Use when Codex must clean Chinese or mixed Chinese-English speech, correct product terminology before semantic editing, compress pauses without making speech breathless, preserve source resolution and frame rate, normalize dialogue loudness, generate final-audio subtitle artifacts, or benchmark local output against ChatCut/video-use/chengfeng/AI剪口播.

Why use Ra Local Talking Head Cut on TypingMind?

Because you install it once and use it with any model. Ra Local Talking Head Cut is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ra Local Talking Head Cut in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Pluviobyte/rnskill/tree/main/skills/ra-local-talking-head-cut. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ra Local Talking Head Cut?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ra Local Talking Head Cut?

As many as you like. As long as a model supports skills, you can use Ra Local Talking Head Cut with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ra Local Talking Head Cut AI skill free?

It is published on GitHub by Pluviobyte. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇